Nonparametric and Semiparametric Volatility Models: Specification, Estimation, and Testing
In recent years, an extensive literature has developed on studying the volatility in financial markets. The simplest approach in this literature regards volatility as a time-invariant constant parameter σ. However, this is contradicted in some of the real world financial data, where a specific patte...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2012
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| Online Access: | https://ink.library.smu.edu.sg/soe_research/1366 https://search.library.smu.edu.sg/primo-explore/fulldisplay?docid=TN_wilbooks10.1002/9781118272039.ch11&context=PC&vid=SMU_NUI&search_scope=Everything&tab=default_tab&lang=en_US |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | In recent years, an extensive literature has developed on studying the volatility in financial markets. The simplest approach in this literature regards volatility as a time-invariant constant parameter σ. However, this is contradicted in some of the real world financial data, where a specific pattern of return variability is observed. These changes are often referred to as the volatility clustering and as first noted by Mandelbrot (1963), this is the property of prices that "large changes tend to be followed by large changes—of either sign—and small changes tend to be followed by small changes." As a consequence, there has been a concerted attempt to model this time-varying volatility. |
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